Our results confirmed that blood-related diffusion mechanism is a biologically endogenous source of hair cortisol.

Hyperspectral imaging (HSI) classification has become a popular research topic in recent years, and effective feature extraction is an important step before the classification task. Traditionally, spectral feature extraction techniques are applied to the HSI data cube directly. This paper presents a novel algorithm for HSI feature extraction by exploiting the curvelet transformed domain via a relatively new spectral feature processing technique -singular spectrum analysis (SSA). Although the wavelet transform has been widely applied for HSI data analysis, the curvelet transform is employed in this paper since it is able to separate image geometric details and background noise effectively. Using the support vector machine (SVM) classifier, experimental results have shown that features extracted by SSA on curvelet coefficients have better performance in terms of classification accuracies over features extracted on wavelet coefficients. Since the proposed approach mainly relies on SSA for feature extraction on the spectral dimension, it actually belongs to the spectral feature extraction category. Therefore, the proposed method has also been compared with some state-of-the-art spectral feature extraction techniques to show its efficacy. In addition, it has been proven that the proposed method is able to remove the undesirable artefacts introduced during the data acquisition process as well. By adding an extra spatial post-processing step to the classified map achieved using the proposed approach, we have shown that the classification performance is comparable with several recent spectral-spatial classification methods.Index Terms-Hyperspectral imaging (HSI), the curvelet transform, singular spectrum analysis (SSA), classification, support vector machine (SVM), spatial post-processing.Manuscript received on October xx, 2015. This work is partially funded by the following projects:

No abstract

No abstract

Classification of hyperspectral images (HSI) has been a challenging problem under active investigation for years especially due to the extremely high data dimensionality and limited number of samples available for training. It is found that hyperspectral image classification can be generally improved only if the feature extraction technique and the classifier are both addressed. In this paper, a novel classification framework for hyperspectral images based on the joint bilateral filter and sparse representation classification (SRC) is proposed. By employing the first principal component as the guidance image for the joint bilateral filter, spatial features can be extracted with minimum edge blurring thus improving the quality of the band-to-band images. For this reason, the performance of the joint bilateral filter has shown better than that of the conventional bilateral filter in this work. In addition, the spectral similarity-based joint SRC (SS-JSRC) is proposed to overcome the weakness of the traditional JSRC method. By combining the joint bilateral filtering and SS-JSRC together, the superiority of the proposed classification framework is demonstrated with respect to several state-of-the-art spectral-spatial classification approaches commonly employed in the HSI community, with better classification accuracy and Kappa coefficient achieved.

No abstract

This version is available at https://strathprints.strath.ac.uk/50084/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Effective compression of hyperspectral imagery using an improved 3DDCT approach for land cover analysis in remote sensing applications Effective compression of hyperspectral imagery using an improved 3D DCT approach for land cover analysis in remote sensing applicationsAlthough hyperspectral imagery (HSI), which has been applied in a wide range of applications, suffers from very large volumes of data, its uncompressed representation is still preferred to avoid compression loss for accurate data analysis. In this paper, we focus on quality-assured lossy compression of HSI,where the accuracy of analysis from decoded data is taken as a key criterion to

Purpose The doctor–patient relationship (DPR) in People’s Republic of China is very tense. This study aimed to provide some explanation by exploring factors influencing the DPR from doctors’ and patients’ perspectives. Methods A cross-sectional study was conducted in one provincial and one city-level general public hospital in Inner Mongolia Autonomous Region of People’s Republic of China. The Difficult Doctor–Patient Relationship Questionnaire (DDPRQ-10) and the Patient–Doctor Relationship Questionnaire (PDRQ-9) were used to assess the quality of the DPR from 226 doctors, and 713 patients’ perspectives, respectively. Multivariate linear regression was used to identify factors significantly associated with the doctors’ and patients’ perceptions of the DPR by assessing coefficients of total effect and their 95% confidence interval. Results The result revealed that provincial-level doctors had a higher DDPRQ-10 score than city-level doctors. Worse DDPRQ-10 scores were seen for doctors who worked in the Internal Medicine departments were aged between 31 and 40 years, held a master’s degree, were dissatisfied with their income, worked more than 40 hrs per week, felt pressure at work, considered the hospital environment to be bad, often felt affected by negative media reports and had defensive behaviors. Patients visiting the provincial hospital had a lower PDRQ-9 score than those from the city-level hospital. Lower PDRQ-9 scores were also seen for patients who were of Mongolian ethnicity, were dissatisfied with their income, waited longer to see the doctor, had a shorter doctor consultation time, had lower expectations of their treatment result, had a low level of trust in the doctor, regarded the hospital environment as bad and those who were frequently influenced by negative media reports. Conclusion This study may provide a useful model to raise the quality of the DPR and to supply evidence for health policy makers and administrators to formulate strategies for reducing the problem of tense DPR in Chinese hospitals.